Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/10577#discussion_r49912654
--- Diff:
sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/planning/patterns.scala
---
@@ -170,17 +173,28 @@ object ExtractFiltersAndInnerJoins extends
PredicateHelper {
}
}
+
/**
* A pattern that collects all adjacent unions and returns their children
as a Seq.
*/
object Unions {
def unapply(plan: LogicalPlan): Option[Seq[LogicalPlan]] = plan match {
- case u: Union => Some(collectUnionChildren(u))
+ case u: Union => Some(collectUnionChildren(mutable.Stack(u),
Seq.empty[LogicalPlan]))
case _ => None
}
- private def collectUnionChildren(plan: LogicalPlan): Seq[LogicalPlan] =
plan match {
- case Union(l, r) => collectUnionChildren(l) ++ collectUnionChildren(r)
- case other => other :: Nil
+ @tailrec
+ private def collectUnionChildren(
+ plans: mutable.Stack[LogicalPlan],
+ children: Seq[LogicalPlan]): Seq[LogicalPlan] = {
+ if (plans.isEmpty) children
+ else {
+ plans.pop match {
+ case Union(grandchildren) =>
+ grandchildren.reverseMap(plans.push(_))
--- End diff --
Using a stack is for doing a depth-first tree traversal. For example, the
users might expect the order of unions in the following two cases should be the
same? Or they might not care it?
```
val unionQuery1 = Union(Union(testRelation, testRelation2),
testRelation)
val unionQuery2 = Union(testRelation, Union(testRelation2,
testRelation))
```
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